Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen3_vl
image-text-to-text
multimodal embedding
qwen
embedding
Instructions to use tomaarsen/Qwen3-VL-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/Qwen3-VL-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/Qwen3-VL-Embedding-8B") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use tomaarsen/Qwen3-VL-Embedding-8B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tomaarsen/Qwen3-VL-Embedding-8B") model = AutoModelForMultimodalLM.from_pretrained("tomaarsen/Qwen3-VL-Embedding-8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a02a654faee1ef4a9cfeb72287550ac09bdd3f83dd4b6f0b6ca627c4e7b0ed32
- Size of remote file:
- 5 GB
- SHA256:
- 79ef275ec5f751d5fb59357c00d473268f9fd74abf5e38aa30137d268e7733c4
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